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MPP-Qwen-Next: Multimodal Pipeline Parallel based on QwenLM

https://github.com/Coobiw/MiniGPT4Qwen/assets/48615375/963416dd-fd97-4680-b7ac-fa4a14beaaae

<video controls> <source src="https://github.com/Coobiw/MiniGPT4Qwen/assets/48615375/963416dd-fd97-4680-b7ac-fa4a14beaaae" type="video/mp4"> Your browser does not support the video tag. </video>

https://github.com/Coobiw/MiniGPT4Qwen/assets/48615375/0e7c33f6-33d3-478a-ab0e-ecc116aeec78

<video controls> <source src="https://github.com/Coobiw/MiniGPT4Qwen/assets/48615375/0e7c33f6-33d3-478a-ab0e-ecc116aeec78" type="video/mp4"> Your browser does not support the video tag. </video>

News

Framework

Features

图像-单轮问答

图像-多轮对话

视频-对话

多图-对话(未经过多图sft,视频sft后涌现该能力)


无视频sft的MPP-14B模型多图对话(看似回答,实际啥都没说):


视频sft后的MPPQwen-8B模型(具备比较不同图像的能力):

TODO LIST

Installation

conda create -n minigpt4qwen python=3.8 && conda activate minigpt4qwen
pip install -e .

Weight&Data Preparation

请放在cache目录中,结构如下

模型权重请参照:WEIGHT.md

训练数据请参照:DATA.md

推理

请先按照WEIGHT.md配置好权重

并在以下链接中二选一,下载sft后的模型权重(15GB):

运行命令行demo

Single-GPU Inference

python cli_demo.py --model-type qwen7b_chat -c lavis/output/pp_7b_video/sft_video/global_step2005/unfreeze_llm_model.pth

MultiGPU(llm使用device_map="auto"加载,可以多卡加载LLM部分模型:

python cli_demo.py --model-type qwen7b_chat -c lavis/output/pp_7b_video/sft_video/global_step2005/unfreeze_llm_model.pth --llm_device_map "auto"

CPU(速度慢):

python cli_demo.py--model-type qwen7b_chat -c lavis/output/pp_7b_video/sft_video/global_step2005/unfreeze_llm_model.pth --cpu-only # 如果显存足够(>=20GB)可以不要--cpu-only

运行后需要输入图片路径,可以输入多张图片,用:f结束图片路径输入后进入对话

常见操作:

:help 查看help

:clear 清空当前命令行

:clh 清空对话历史(但图像输入不会更改)

:his 查看对话历史

:img 查看输入的图像路径

运行gradio webui demo

Single-GPU Inference

python webui_demo.py --model-type qwen7b_chat -c lavis/output/pp_7b_video/sft_video/global_step2005/unfreeze_llm_model.pth

MultiGPU(llm使用device_map="auto"加载

python webui_demo.py --model-type qwen7b_chat -c lavis/output/pp_7b_video/sft_video/global_step2005/unfreeze_llm_model.pth --llm_device_map "auto"

CPU:

python webui_demo.py --model-type qwen7b_chat -c lavis/output/pp_7b_video/sft_video/global_step2005/unfreeze_llm_model.pth --cpu-only # 如果显存足够(>=20GB)可以不要--cpu-only

流水线并行训练(PP+DP)

下面为8卡3090运行指令:

Pretrain

nproc_per_node: 8 dp: 4 pp: 2 nproc_per_node = pp * dp

python -m torch.distributed.run --nproc_per_node=8 train_pipeline.py --cfg-path lavis/projects/pp_qwen7b_video/pretrain.yaml --num-stages 2

SFT

nproc_per_node: 8 dp: 1 pp: 8 nproc_per_node = pp * dp

python -m torch.distributed.run --nproc_per_node=8 train_pipeline.py --cfg-path lavis/projects/pp_qwen7b_video/sft.yaml --num-stages 8

pipeline parallel的权重转换为pth文件

预训练阶段:

(仅转换linear projection层)

python pipe_proj2pth.py --ckpt-dir lavis/output/pp_7b_video/pretrain/global_step2181

转换后,模型文件会存储在ckpt_dir底下,名为model.pth

sft阶段

(需要转换projection层和所有LLM的参数)

python pipemodel2pth.py --ckpt-dir lavis/output/pp_7b_video/sft_video/global_step2005

转换后,模型文件会存储在ckpt_dir底下,名为unfreeze_llm_model.pth

二阶段训练loss曲线参考


pretrain:


sft:

Custom Data Format(如果你想continue training)

处理函数可以参考: https://github.com/Coobiw/MiniGPT4Qwen/releases/download/MPP-Qwen-Next_ckpt-and-data/ckpt-and-data.zip中,llava_instuct和videochatgpt目录里的analysis.py脚本

P.S.: 如果路径经常出错,可以把所有路径都改成绝对路径(包括dataset configs)

图像指令微调数据格式

单轮(instruction和output为str):

[
    {
        "image": "000000215677.jpg",
        "instruction": "<Img><ImageHere></Img> {question}",
        "output": "{answer}"
    },
]

多轮(instruction和output为等长的list):

{
        "image": "000000479443.jpg",
        "instruction": [
            "<Img><ImageHere></Img> {question1}",
            "{question2}",
            "..."
        ],
        "output": [
            "{answer1}",
            "{answer2}",
            "..."
        ]
    },

视频指令微调数据格式

[
    {
        "video": "v_k_ZXmr8pmrs.mkv",
        "instruction": "<Img><ImageHere></Img> {question}",
        "output": "{answer}"
    }
]

Acknowledgement

License

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